World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources:Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Vantor imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Vantor products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. Vantor Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Vantor HD. Imagery UpdatesYou can use the Updates Mode in the World Imagery Wayback app to learn more about recent and pending updates. Accessing this information requires a user login with an ArcGIS organizational account. CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map. UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map. FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. The map features 0.5m resolution imagery in the continental United States and parts of Western Europe from DigitalGlobe. Additional DigitalGlobe sub-meter imagery is featured in many parts of the world. In the United States, 1 meter or better resolution NAIP imagery is available in some areas. In other parts of the world, imagery at different resolutions has been contributed by the GIS User Community. In select communities, very high resolution imagery (down to 0.03m) is available down to ~1:280 scale. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. View the list of Contributors for the World Imagery Map.CoverageView the links below to learn more about recent updates and map coverage:What's new in World ImageryWorld coverage mapCitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map. A similar raster web map, Imagery with Labels, is also available.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

This dataset provides a seamless cloud-free 10m resolution satellite imagery layer of the New Zealand mainland and offshore islands.

The imagery was captured by the European Space Agency Sentinel-2 satellites between September 2022 - April 2023.

Data comprises: • 450 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:50000 tile layout. • Satellite sensors: ESA Sentinel-2A and Sentinel-2B • Acquisition dates: September 2022 - April 2023 • Spectral resolution: R, G, B • Spatial resolution: 10 meters • Radiometric resolution: 8-bits (downsampled from 12-bits)

This is a visual product only. The data has been downsampled from 12-bits to 8-bits, and the original values of the images have been modified for visualisation purposes.

Also available on: • Basemaps • NZ Imagery - Registry of Open Data on AWS

High resolution orthorectified images combine the image characteristics of an aerial photograph with the geometric qualities of a map. An orthoimage is a uniform-scale image where corrections have been made for feature displacement such as building tilt and for scale variations caused by terrain relief, sensor geometry, and camera tilt. A mathematical equation based on ground control points, sensor calibration information, and a digital elevation model is applied to each pixel to rectify the image to obtain the geometric qualities of a map.

A digital orthoimage may be created from several photographs mosaicked to form the final image. The source imagery may be black-and-white, natural color, or color infrared with a pixel resolution of 1-meter or finer. With orthoimagery, the resolution refers to the distance on the ground represented by each pixel.

This dataset contains Landsat 5 imagery for selected areas of Queensland, currently Torres Strait and around Lizard Island and Cape Tribulation.

This collection was made as a result of the development of the Torres Strait Features dataset. It includes a number (typically 4 - 8) of selected Landsat images for each scene from the entire Landsat 5 archive. These images were selected for having low cloud cover and clear water. The aim of this collection was to allow investigation of the marine features.

The complete catalogue of Landsat 5 for scenes 96_70, 96_71, 97_67, 97_68, 98_66, 98_67, 98_68_99_66, 99_67 and 99_68 were downloaded from the Google Earth Engine site ( https://console.developers.google.com/storage/earthengine-public/landsat/ ). The images were then processed into low resolution true colour using GDAL. They were then reviewed for picture clarity and the best ones were selected and processed at full resolution to be part of this collection.

The true colour conversion was achieved by applying level adjustment to each channel to ensure that the tonal scaling of each channel was adjusted to give a good overall colour balance. This effectively set the black point of the channel and the gain. This adjustment was applied consistently to all images.

• Red: Channel B3, Black level 8, White level 58
• Green: Channel B2, Black level 10, White level 55
• Blue: Channel B1, Black level 32, White level 121

Note: A constant level adjustment was made to the images regardless of the time of the year that the images were taken. As a result images in the summer tend to be brighter than those in the winter.

After level adjustment the three channels were merged into a single colour image using gdal_merge. The black surround on the image was then made transparent using the GDAL nearblack command.

This collection consists of 59 images saved as 4 channel (Red, Green, Blue, Alpha) GeoTiff images with LZW compression (lossless) and internal overviews with a WGS 84 UTM 54N projection.

Each of the individual images can be downloaded from the eAtlas map client (Overlay layers: eAtlas/Imagery Base Maps Earth Cover/Landsat 5) or as a collection of all images for each scene.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\NERP-TE\13.1_eAtlas\QLD_NERP-TE-13-1_eAtlas_Landsat-5_1988-2011

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

The Ontario Imagery Web Map Service (OIWMS) is an open data service available to everyone free of charge. It provides instant online access to the most recent, highest quality, province wide imagery. GEOspatial Ontario (GEO) makes this data available as an Open Geospatial Consortium (OGC) compliant web map service or as an ArcGIS map service. Imagery was compiled from many different acquisitions which are detailed in the Ontario Imagery Web Map Service Metadata Guide linked below. Instructions on how to use the service can also be found in the Imagery User Guide linked below. Note: This map displays the Ontario Imagery Web Map Service Source, a companion ArcGIS web map service to the Ontario Imagery Web Map Service. It provides an overlay that can be used to identify acquisition relevant information such as sensor source and acquisition date. OIWMS contains several hierarchical layers of imagery, with coarser less detailed imagery that draws at broad scales, such as a province wide zooms, and finer more detailed imagery that draws when zoomed in, such as city-wide zooms. The attributes associated with this data describes at what scales (based on a computer screen) the specific imagery datasets are visible. Available Products Ontario Imagery OGC Web Map Service – public linkOntario Imagery ArcGIS Map Service – public linkOntario Imagery Web Map Service Source – public linkOntario Imagery ArcGIS Map Service – OPS internal linkOntario Imagery Web Map Service Source – OPS internal linkAdditional Documentation Ontario Imagery Web Map Service Metadata Guide (PDF)Ontario Imagery Web Map Service Copyright Document (PDF) Imagery User Guide (Word)StatusCompleted: Production of the data has been completed Maintenance and Update FrequencyAnnually: Data is updated every year ContactOntario Ministry of Natural Resources, Geospatial Ontario, imagery@ontario.ca

Multispectral imagery captured by Sentinel-2 satellites, featuring 13 spectral bands (visible, near-infrared, and short-wave infrared). Available globally since 2018 (Europe since 2017) with 10-60 m spatial resolution and revisit times of 2-3 days at mid-latitudes. Accessible through the EOSDA LandViewer platform for visualization, analysis, and download.

This dataset collection contains A0 maps of the Keppel Island region based on satellite imagery and fine-scale habitat mapping of the islands and marine environment. This collection provides the source satellite imagery used to produce these maps and the habitat mapping data.

The imagery used to produce these maps was developed by blending high-resolution imagery (1 m) from ArcGIS Online with a clear-sky composite derived from Sentinel 2 imagery (10 m). The Sentinel 2 imagery was used to achieve full coverage of the entire region, while the high-resolution was used to provide detail around island areas.

The blended imagery is a derivative product of the Sentinel 2 imagery and ArcGIS Online imagery, using Photoshop to to manually blend the best portions of each imagery into the final product. The imagery is provided for the sole purpose of reproducing the A0 maps.

Methods:

The high resolution satellite composite composite was developed by manual masking and blending of a Sentinel 2 composite image and high resolution imagery from ArcGIS Online World Imagery (2019).

The Sentinel 2 composite was produced by statistically combining the clearest 10 images from 2016 - 2019. These images were manually chosen based on their very low cloud cover, lack of sun glint and clear water conditions. These images were then combined together to remove clouds and reduce the noise in the image.

The processing of the images was performed using a script in Google Earth Engine. The script combines the manually chosen imagery to estimate the clearest imagery. The dates of the images were chosen using the EOBrowser (https://www.sentinel-hub.com/explore/eobrowser) to preview all the Sentinel 2 imagery from 2015-2019. The images that were mostly free of clouds, with little or no sun glint, were recorded. Each of these dates was then viewed in Google Earth Engine with high contrast settings to identify images that had high water surface noise due to algal blooms, waves, or re-suspension. These were excluded from the list. All the images were then combined by applying a histogram analysis of each pixel, with the final image using the 40th percentile of the time series of the brightness of each pixel. This approach helps exclude effects from clouds.

The contrast of the image was stretched to highlight the marine features, whilst retaining detail in the land features. This was done by choosing a black point for each channel that would provide a dark setting for deep clear water. Gamma correction was then used to lighten up the dark water features, whilst not ove- exposing the brighter shallow areas.

Both the high resolution satellite imagery and Sentinel 2 imagery was combined at 1 m pixel resolution. The resolution of the Sentinel 2 tiles was up sampled to match the resolution of the high-resolution imagery. These two sets of imagery were then layered in Photoshop. The brightness of the high-resolution satellite imagery was then adjusting to match the Sentinel 2 imagery. A mask was then used to retain and blend the imagery that showed the best detail of each area. The blended tiles were then merged with the overall area imagery by performing a GDAL merge, resulting in an upscaling of the Sentinel 2 imagery to 1 m resolution.


Habitat Mapping:

A 5 m resolution habitat mapping was developed based on the satellite imagery, aerial imagery available, and monitoring site information. This habitat mapping was developed to help with monitoring site selection and for the mapping workshop with the Woppaburra TOs on North Keppel Island in Dec 2019.

The habitat maps should be considered as draft as they don't consider all available in water observations. They are primarily based on aerial and satellite images.

The habitat mapping includes: Asphalt, Buildings, Mangrove, Cabbage-tree palm, Sheoak, Other vegetation, Grass, Salt Flat, Rock, Beach Rock, Gravel, Coral, Sparse coral, Unknown not rock (macroalgae on rubble), Marine feature (rock).

This assumed layers allowed the digitisation of these features to be sped up, so for example, if there was coral growing over a marine feature then the boundary of the marine feature would need to be digitised, then the coral feature, but not the boundary between the marine feature and the coral. We knew that the coral was going to cut out from the marine feature because the coral is on top of the marine feature, saving us time in digitising this boundary. Digitisation was performed on an iPad using Procreate software and an Apple pencil to draw the features as layers in a drawing. Due to memory limitations of the iPad the region was digitised using 6000x6000 pixel tiles. The raster images were converted back to polygons and the tiles merged together.

A python script was then used to clip the layer sandwich so that there is no overlap between feature types.

Habitat Validation:

Only limited validation was performed on the habitat map. To assist in the development of the habitat mapping, nearly every YouTube video available, at the time of development (2019), on the Keppel Islands was reviewed and, where possible, georeferenced to provide a better understanding of the local habitats at the scale of the mapping, prior to the mapping being conducted. Several validation points were observed during the workshop. The map should be considered as largely unvalidated.

data/coastline/Keppels_AIMS_Coastline_2017.shp:
The coastline dataset was produced by starting with the Queensland coastline dataset by DNRME (Downloaded from http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={369DF13C-1BF3-45EA-9B2B-0FA785397B34} on 31 Aug 2019). This was then edited to work at a scale of 1:5000, using the aerial imagery from Queensland Globe as a reference and a high-tide satellite image from 22 Feb 2015 from Google Earth Pro. The perimeter of each island was redrawn. This line feature was then converted to a polygon using the "Lines to Polygon" QGIS tool. The Keppel island features were then saved to a shapefile by exporting with a limited extent.

data/labels/Keppel-Is-Map-Labels.shp:
This contains 70 named places in the Keppel island region. These names were sourced from literature and existing maps. Unfortunately, no provenance of the names was recorded. These names are not official. This includes the following attributes:
- Name: Name of the location. Examples Bald, Bluff
- NameSuffix: End of the name which is often a description of the feature type: Examples: Rock, Point
- TradName: Traditional name of the location
- Scale: Map scale where the label should be displayed.

data/lat/Keppel-Is-Sentinel2-2016-19_B4-LAT_Poly3m_V3.shp:
This corresponds to a rough estimate of the LAT contours around the Keppel Islands. LAT was estimated from tidal differences in Sentinel-2 imagery and light penetration in the red channel. Note this is not very calibrated and should be used as a rough guide. Only one rough in-situ validation was performed at low tide on Ko-no-mie at the edge of the reef near the education centre. This indicated that the LAT estimate was within a depth error range of about +-0.5 m.

data/habitat/Keppels_AIMS_Habitat-mapping_2019.shp:
This shapefile contains the mapped land and marine habitats. The classification type is recorded in the Type attribute.

Format:

GeoTiff (Internal JPEG format - 538 MB)
PDF (A0 regional maps - ~30MB each)
Shapefile (Habitat map, Coastline, Labels, LAT estimate)

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Keppels_AIMS_Regional-maps

World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources:Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Maxar imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Maxar products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. Maxar Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Maxar HD. Imagery UpdatesYou can use the Updates Mode in the World Imagery Wayback app to learn more about recent and pending updates. Accessing this information requires a user login with an ArcGIS organizational account. CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map. UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map. FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources:Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Vantor imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Vantor products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. Vantor Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Vantor HD. Imagery UpdatesYou can use the Updates Mode in the World Imagery Wayback app to learn more about recent and pending updates. Accessing this information requires a user login with an ArcGIS organizational account. CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map. UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map. FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

This map features the World Imagery map, focused on the continent of Africa. World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. DigitalGlobe sub-meter imagery is featured in many parts of the world, including Africa. Sub-meter Pléiades imagery is available in select urban areas. Additionally, imagery at different resolutions has been contributed by the GIS User Community.For more information on this map, view the World Imagery item description. Metadata: This service is metadata-enabled. With the Identify tool in ArcMap or the World Imagery with Metadata web map, you can see the resolution, collection date, and source of the imagery at the location you click. Values of "99999" mean that metadata is not available for that field. The metadata applies only to the best available imagery at that location. You may need to zoom in to view the best available imagery.Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

This dataset consists of collections of satellite image composites (Sentinel 2 and Landsat 8) that are created from manually curated image dates for a range of projects. These images are typically prepared for subsequent analysis or testing of analysis algorithms as part of other projects. This dataset acts as a repository of reproducible test sets of images processed from Google Earth Engine using a standardised workflow.

Details of the algorithms used to produce the imagery are described in the GEE code and code repository available on GitHub (https://github.com/eatlas/World_AIMS_Marine-satellite-imagery).


Project test image sets:

As new projects are added to this dataset, their details will be described here:

- NESP MaC 2.3 Benthic reflection estimation (projects/CS_NESP-MaC-2-3_AIMS_Benth-reflect):
This collection consists of six Sentinel 2 image composites in the Coral Sea and GBR for the purpose of testing a method of determining benthic reflectance of deep lagoonal areas of coral atolls. These image composites are in GeoTiff format, using 16-bit encoding and LZW compression. These images do not have internal image pyramids to save on space.
[Status: final and available for download]

- NESP MaC 2.3 Oceanic Vegetation (projects/CS_NESP-MaC-2-3_AIMS_Oceanic-veg):
This project is focused on mapping vegetation on the bottom of coral atolls in the Coral Sea. This collection consists of additional images of Ashmore Reef. The lagoonal area of Ashmore has low visibility due to coloured dissolved organic matter, making it very hard to distinguish areas that are covered in vegetation. These images were manually curated to best show the vegetation. While these are the best images in the Sentinel 2 series up to 2023, they are still not very good. Probably 80 - 90% of the lagoonal benthos is not visible.
[Status: final and available for download]

- NESP MaC 3.17 Australian reef mapping (projects/AU_NESP-MaC-3-17_AIMS_Reef-mapping):
This collection of test images was prepared to determine if creating a composite from manually curated image dates (corresponding to images with the clearest water) would produce a better composite than a fully automated composite based on cloud filtering. The automated composites are described in https://doi.org/10.26274/HD2Z-KM55. This test set also includes composites from low tide imagery. The images in this collection are not yet available for download as the collection of images that will be used in the analysis has not been finalised.
[Status: under development, code is available, but not rendered images]

- Capricorn Regional Map (projects/CapBunk_AIMS_Regional-map): This collection was developed for making a set of maps for the region to facilitate participatory mapping and reef restoration field work planning.
[Status: final and available for download]

- Default (project/default): This collection of manual selected scenes are those that were prepared for the Coral Sea and global areas to test the algorithms used in the developing of the original Google Earth Engine workflow. This can be a good starting point for new test sets. Note that the images described in the default project are not rendered and made available for download to save on storage space.
[Status: for reference, code is available, but not rendered images]


Filename conventions:

The images in this dataset are all named using a naming convention. An example file name is Wld_AIMS_Marine-sat-img_S2_NoSGC_Raw-B1-B4_54LZP.tif. The name is made up of:
- Dataset name (Wld_AIMS_Marine-sat-img), short for World, Australian Institute of Marine Science, Marine Satellite Imagery.
- Satellite source: L8 for Landsat 8 or S2 for Sentinel 2.
- Additional information or purpose: NoSGC - No sun glint correction, R1 best reference imagery set or R2 second reference imagery.
- Colour and contrast enhancement applied (DeepFalse, TrueColour,Shallow,Depth5m,Depth10m,Depth20m,Raw-B1-B4),
- Image tile (example: Sentinel 2 54LZP, Landsat 8 091086)


Limitations:

Only simple atmospheric correction is applied to land areas and as a result the imagery only approximates the bottom of atmosphere reflectance.

For the sentinel 2 imagery the sun glint correction algorithm transitions between different correction levels from deep water (B8) to shallow water (B11) and a fixed atmospheric correction for land (bright B8 areas). Slight errors in the tuning of these transitions can result in unnatural tonal steps in the transitions between these areas, particularly in very shallow areas.

For the Landsat 8 image processing land areas appear as black from the sun glint correction, which doesn't separately mask out the land. The code for the Landsat 8 imagery is less developed than for the Sentinel 2 imagery.

The depth contours are estimated using satellite derived bathymetry that is subject to errors caused by cloud artefacts, substrate darkness, water clarity, calibration issues and uncorrected tides. They were tuned in the clear waters of the Coral Sea. The depth contours in this dataset are RAW and contain many false positives due to clouds. They should not be used without additional dataset cleanup.



Change log:

As changes are made to the dataset, or additional image collections are added to the dataset then those changes will be recorded here.

2nd Edition, 2024-06-22: CapBunk_AIMS_Regional-map
1st Edition, 2024-03-18: Initial publication of the dataset, with CS_NESP-MaC-2-3_AIMS_Benth-reflect, CS_NESP-MaC-2-3_AIMS_Oceanic-veg and code for AU_NESP-MaC-3-17_AIMS_Reef-mapping and Default projects.


Data Format:

GeoTiff images with LZW compression. Most images do not have internal image pyramids to save on storage space. This makes rendering these images very slow in a desktop GIS. Pyramids should be added to improve performance.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Wld-AIMS-Marine-sat-img

World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. The map features 0.5m resolution imagery in the continental United States and parts of Western Europe from Vantor. Additional Vantor sub-meter imagery is featured in many parts of the world. In the United States, 1 meter or better resolution NAIP imagery is available in some areas. In other parts of the world, imagery at different resolutions has been contributed by the GIS User Community. In select communities, very high resolution imagery (down to 0.03m) is available down to ~1:280 scale. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. View the list of Contributors for the World Imagery Map. See World Imagery for more information on this map. Metadata: Point and click on the map to see the resolution, collection date, and source of the imagery. Values of "99999" mean that metadata is not available for that field. The metadata applies only to the best available imagery at that location. You may need to zoom in to view the best available imagery. Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates. Need Newer Imagery?: If you need to access more recent or higher resolution imagery, you can find and order that in the Content Store for ArcGIS app.Learn MoreGet AccessOpen App

World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources:Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Vantor imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Vantor products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. Vantor Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Vantor HD. Imagery UpdatesYou can use the Updates Mode in the World Imagery Wayback app to learn more about recent and pending updates. Accessing this information requires a user login with an ArcGIS organizational account. CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map. UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map. FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

This map features the World Imagery map, focused on the Carribean region. World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. DigitalGlobe sub-meter imagery is featured in many parts of the world, including Africa. Sub-meter Pléiades imagery is available in select urban areas. Additionally, imagery at different resolutions has been contributed by the GIS User Community.For more information on this map, view the World Imagery item description. Metadata: This service is metadata-enabled. With the Identify tool in ArcMap or the World Imagery with Metadata web map, you can see the resolution, collection date, and source of the imagery at the location you click. Values of "99999" mean that metadata is not available for that field. The metadata applies only to the best available imagery at that location. You may need to zoom in to view the best available imagery.Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

Detecting Landscape Objects on Satellite Images with Artificial Intelligence In recent years, there has been a significant increase in the use of artificial intelligence (AI) for image recognition and object detection. This technology has proven to be useful in a wide range of applications, from self-driving cars to facial recognition systems. In this project, the focus lies on using AI to detect landscape objects in satellite images (aerial photography angle) with the goal to create an annotated map of The Netherlands with all the coordinates of the given landscape objects.

Background Information

Problem Statement One of the things that Naturalis does is conducting research into the distribution of wild bees (Naturalis, n.d.). For their research they use a model that predicts whether or not a certain species can occur at a given location. Representing the real world in a digital form, there is at the moment not yet a way to generate an inventory of landscape features such as presence of trees, ponds and hedges, with their precise location on the digital map. The current models rely on species observation data and climate variables, but it is expected that adding detailed physical landscape information could increase the prediction accuracy. Common maps do not contain this level of detail, but high-resolution satellite images do.

Possible opportunities Based on the problem statement, there is at the moment at Naturalis not a map that does contain the level of detail where detection of landscape elements could be made, according to their wishes. The idea emerged that it should be possible to use satellite images to find the locations of small landscape elements and produce an annotated map. Therefore, by refining the accuracy of the current prediction model, researchers can gain a profound understanding of wild bees in the Netherlands with the goal to take effective measurements to protect wild bees and their living environment.

Goal of project The goal of the project is to develop an artificial intelligence model for landscape detection on satellite images to create an annotated map of The Netherlands that would therefore increase the accuracy prediction of the current model that is used at Naturalis. The project aims to address the problem of a lack of detailed maps of landscapes that could revolutionize the way Naturalis conduct their research on wild bees. Therefore, the ultimate aim of the project in the long term is to utilize the comprehensive knowledge to protect both the wild bees population and their natural habitats in the Netherlands.

Data Collection Google Earth One of the main challenges of this project was the difficulty in obtaining a qualified dataset (with or without data annotation). Obtaining high-quality satellite images for the project presents challenges in terms of cost and time. The costs in obtaining high-quality satellite images of the Netherlands is 1,038,575 $ in total (for further details and information of the costs of satellite images. On top of that, the acquisition process for such images involves various steps, from the initial request to the actual delivery of the images, numerous protocols and processes need to be followed.

After conducting further research, the best possible solution was to use Google Earth as the primary source of data. While Google Earth is not allowed to be used for commercial or promotional purposes, this project is for research purposes only for Naturalis on their research of wild bees, hence the regulation does not apply in this case.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This layer is a subset from the World Imagery to focus on the Pacific Region. You can access World Imagery from here. World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15-meter TerraColor imagery at small and mid-scales (~1:591M down to ~1:288k) for the world. The map features Maxar imagery at 0.3-meter resolution for select metropolitan areas around the world, 0.5-meter resolution across the United States and parts of Western Europe, and 0.6-1.2-meter resolution imagery across the rest of the world. In addition to commercial sources, the World Imagery map features high-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 0.3-meter to 0.03-meter resolution, down to ~1:280 in select communities. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program.Updates and CoverageYou can use the World Imagery Updates app to learn more about recent updates and map coverage.CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.